The intratumoral microbiome is recognized as a core functional element of the tumor microenvironment and has emerged as a frontier research area in oncology. Microorganisms such as bacteria, fungi, and viruses have been detected within tumor tissues and are implicated in regulating multiple aspects of tumor biology. Interest in this field reflects the potential for microbial communities within tumors to modulate disease onset, progression, and patient outcomes.
Microorganisms within tumors interact with cancer cells and host tissues through several reported mechanisms. These include secretion of microbial metabolites that can alter cellular signaling and metabolism, induction of genomic instability in host cells, and remodeling of the immune microenvironment. Through such pathways, intratumoral microbes can participate in the regulation of tumor biological mechanisms; however, the precise molecular details and causal relationships often depend on the specific microbial taxa and tumor context described in the literature.
Studies summarized in the review indicate that microbial species composition and abundance within tumors show distinct cancer-type specificity. That is, the taxa and relative loads found in one tumor type may differ substantially from those in another. Correspondingly, the impact of intratumoral microbes on patient prognosis is highly context dependent: associations vary by tumor type, microbial species, host factors, and the tumor microenvironment. The review emphasizes that prognostic effects cannot be generalized and must be interpreted within the specific clinical and biological context in which they are observed.
The review categorizes current detection approaches into three major domains:
In situ detection technologies — these methods permit localization of microbes within tissue architecture and preserve spatial relationships between microorganisms and host cells.
Metagenomic sequencing — shotgun or targeted metagenomic methods enable broad taxonomic profiling and, in many cases, functional inference from nucleic acid sequences extracted from tumor samples.
Computational pathology–driven intelligent detection — approaches that apply image analysis and machine learning to pathology data to identify microbial signals, quantify abundance, and resolve spatial distribution.
Each class of methods contributes complementary information: in situ approaches provide spatial context, metagenomic sequencing offers taxonomic breadth, and computational pathology aims to scale analysis and extract patterns inaccessible to manual review.
The review highlights that each detection modality has inherent advantages and limitations. In situ methods deliver spatial resolution but may be limited in throughput or taxonomic breadth. Metagenomic sequencing affords comprehensive profiling but can be challenged by low microbial biomass in tumor tissues and potential contamination, impacting sensitivity and specificity of detection. Computational pathology and intelligent detection methods can interrogate routine histopathology images at scale but must overcome technical bottlenecks related to identifying low-abundance microbial signals, achieving accurate quantification, and resolving fine-grained spatial distribution within complex tissue structures.
Among computational approaches, the review calls attention to methods centered on deep learning, which are progressively addressing key technical obstacles. Deep learning–based intelligent detection is reported to be making headway in recognizing low-abundance microbial signals in histological data, improving quantification accuracy, and enabling more refined spatial mapping of microbes within tumor tissue. These developments suggest computational pathology may complement molecular assays and in situ methods to provide richer, multimodal characterization of intratumoral microbiomes.
The authors project that the field will advance through deeper integration of three technological streams: three-dimensional pathological imaging, spatial omics, and multi-modal foundation models (large models capable of integrating heterogeneous data types). This convergence is expected to drive multi-dimensional data integration, enabling simultaneous analysis of microbial identity, spatial localization, molecular context, and tissue architecture at increased scale and resolution.
By enabling multi-dimensional characterization of the intratumoral microbiome and its interactions with the host, these technical advances could provide innovative pathways to elucidate regulatory mechanisms and to develop precision diagnostic and therapeutic strategies tailored to an individual tumor’s microecological features. The review frames such developments as potential routes to translate mechanistic knowledge of microbe–host interactions into clinically actionable approaches.
All authors declared no conflicts of interest in the source. Funding sources listed in the article metadata include support from the National Natural Science Foundation of China (grant 82574197) and the Healthy Zhejiang One Million People Cohort (grant K20230085), as recorded in the PubMed entry.
The intratumoral microbiome is an influential and context-dependent component of the tumor microenvironment. Current detection strategies—in situ methods, metagenomic sequencing, and computational pathology—each offer distinct strengths and face specific limitations. Advances in deep learning and integrative multimodal technologies, including 3D pathology and spatial omics, are expected to overcome current technical barriers and to propel research toward precision diagnostics and therapies informed by tumor-resident microbial communities.